This webpage contains R codes and figures from Authors (2022). The entire dataset for data analysis is available in the Open Science Framework platform.
Import
1. Import a .xlsx file
suppressPackageStartupMessages({
library(tidyverse)
library(readxl)
library(tidytext)
library(quanteda.textstats)
library(stm)
library(psych)
library(reshape2)
library(cowplot)
library(stminsights)
library(ggraph)
library(widyr)
library(tidygraph)
library(dplyr)
library(ggplot2)
library(rmarkdown)
library(plotly)
})
raw_tbl <- read_excel("data/raw_data_xl.xlsx")
raw_tbl_period <- raw_tbl %>% mutate(period = dplyr::case_when(
year >=1980 & year <= 1989 ~ "1980_to_1989",
year >=1990 & year <= 1999 ~ "1990_to_1999",
year >=2000 & year <= 2009 ~ "2000_to_2009",
year >=2010 & year <= 2021 ~ "2010_to_2021")) %>%
dplyr::select(reference_type, author, year, period, everything())
paged_table(raw_tbl_period)2. Preprocess
Construct a document-feature matrix (dfm) from a tokens object.
source("scripts/preprocess.all.R", local = TRUE)
toks_processed <- raw_tbl_period %>% preprocess.all(text_col = raw_tbl$abstract, verbose = FALSE)
dfm <- dfm(toks_processed)
dfm_td <- tidy(dfm)
dfm_idf <- dfm_td %>%
bind_tf_idf(term, document, count) %>%
distinct(term, idf) %>%
arrange(idf, as.factor(term))
dfm_idf_5_pct <- dfm_idf %>%
filter(idf < quantile(dfm_idf$idf, 0.05))
paged_table(dfm_idf_5_pct)Remove stop words (common words across documents).
source("scripts/stopwords_list.R", local = TRUE)
toks_processed_removed <-
toks_processed %>%
tokens_remove(stopwords_list)
toks_processed_removed## Tokens consisting of 488 documents and 9 docvars.
## 1 :
## [1] "notes" "alp" "minicalculator"
## [4] "program" "elementary_mathematics" "worked"
## [7] "clinical" "learning_disabled" "youngsters"
## [10] "perceptual" "memory" "deficit"
## [ ... and 19 more ]
##
## 2 :
## [1] "investigated" "relationship" "locus"
## [4] "achievement" "learning_disabled" "elementary_school"
## [7] "aged" "CAI" "instructional_method"
##
## 3 :
## [1] "investigation" "CAI" "learning_disabled"
## [4] "elementary_school" "CAI" "mathematics_skill"
## [7] "twenty" "references" "listed"
##
## 4 :
## [1] "arc" "ed" "curriculum" "video" "game"
## [6] "formats" "language" "arts" "motivational" "feature"
## [11] "feedback" "high"
## [ ... and 12 more ]
##
## 5 :
## [1] "explores" "applicability" "video" "arcade"
## [5] "game" "formats" "educational" "microcomputer"
## [9] "software" "variable" "ore" "potentially"
## [ ... and 39 more ]
##
## 6 :
## [1] "investigation" "handheld" "calculator"
## [4] "secondary" "educable" "mental_retardation"
## [7] "speed" "accuracy" "motivation"
## [10] "working" "algorithmic" "basic"
## [ ... and 143 more ]
##
## [ reached max_ndoc ... 482 more documents ]Construct a second dfm after removing stop words.
dfm_2 <- toks_processed_removed %>% dfm()
dfm_2## Document-feature matrix of: 488 documents, 4,510 features (99.01% sparse) and 9 docvars.
## features
## docs notes alp minicalculator program elementary_mathematics worked clinical
## 1 1 2 1 1 1 1 1
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## features
## docs learning_disabled youngsters perceptual
## 1 2 1 1
## 2 1 0 0
## 3 1 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## [ reached max_ndoc ... 482 more documents, reached max_nfeat ... 4,500 more features ]Structural topic modeling
1. Modeling
Convert quanteda objects to stm package format.
out <- convert(dfm_2, to = "stm")
# set.seed(1234)
# K_search <- searchK(out$documents,
# out$vocab,
# data = out$meta,
# max.em.its = 75,
# init.type = "Spectral",
# K = c(5:30),
# prevalence = ~
# I((year >= 1980)*(year - 1980)) +
# I((year >= 1990)*(year - 1990)) +
# I((year >= 2000)*(year - 2000)) +
# I((year >= 2010)*(year - 2010)),
# verbose = FALSE)
# K_search %>% plot.searchK()
set.seed(1234)
stm_15 <- stm(out$documents,
out$vocab,
data = out$meta,
prevalence = ~
I((year >= 1980)*(year - 1980)) +
I((year >= 1990)*(year - 1990)) +
I((year >= 2000)*(year - 2000)) +
I((year >= 2010)*(year - 2010)),
max.em.its = 75,
init.type = 'Spectral',
K = 15,
verbose = FALSE)
stm_15## A topic model with 15 topics, 488 documents and a 4510 word dictionary.Model estimated effects.
set.seed(1234)
effect_stm_15 <- estimateEffect(1:15 ~
I((year >= 1980)*(year - 1980)) +
I((year >= 1990)*(year - 1990)) +
I((year >= 2000)*(year - 2000)) +
I((year >= 2010)*(year - 2010)),
stm_15,
metadata = out$meta,
documents = out$documents,
uncertainty = "Global")
set.seed(1234)
effect_stm_15_td <- tidy(effect_stm_15)
labeled_topic_list <- c(
"CAI",
"Calculators",
"Visual-based technology",
"Learning disabilities",
"Math achievement",
"Instructional sequence",
"Auditory support",
"Self-monitoring tools",
"Software",
"Robotics",
"Support for visual materials",
"Testing",
"WPS instruction",
"Apps",
"Fractions instruction"
)
names(labeled_topic_list) <- c(1,2,3,4,5,6,7,8,9,10,11,12,13,14, 15)
effect_stm_15_td <- effect_stm_15_td %>%
mutate(topic_label = as.factor(labeled_topic_list[effect_stm_15_td$topic])) %>%
dplyr::select(topic, topic_label, term, estimate, std.error, statistic, p.value)
paged_table(effect_stm_15_td)Tidy the word-topic combinations (per-topic-per-term probabilities).
united_tbl <- read_excel("data/united_tbl.xlsx")
stm_15$vocab <- dplyr::case_when(
stm_15$vocab == "aac" ~ "AAC",
stm_15$vocab == "adhd" ~ "ADHD",
stm_15$vocab == "asd" ~ "ASD",
stm_15$vocab == "assistive_technology" ~ "assistive technology",
stm_15$vocab == "baip" ~ "BAIP",
stm_15$vocab == "basic_skill" ~ "basic skill",
stm_15$vocab == "blended_learning" ~ "blended learning",
stm_15$vocab == "bmt" ~ "BMT",
stm_15$vocab == "cai" ~ "CAI",
stm_15$vocab == "cbm" ~ "CBM",
stm_15$vocab == "ccss" ~ "CCSS",
stm_15$vocab == "concrete_manipulatives" ~ "concrete manipulatives",
stm_15$vocab == "cra" ~ "CRA",
stm_15$vocab == "digital_text" ~ "digital text",
stm_15$vocab == "drill_practice" ~ "drill and practice",
stm_15$vocab == "eai" ~ "EAI",
stm_15$vocab == "ebd" ~ "EBD",
stm_15$vocab == "ebp" ~ "EBP",
stm_15$vocab == "eworkbook" ~ "eWorkbook",
stm_15$vocab == "explicit_instruction" ~ "explicit instruction",
stm_15$vocab == "functional_relation" ~ "functional relation",
stm_15$vocab == "fxs" ~ "FXS",
stm_15$vocab == "graphing_calculator" ~ "graphing calculator",
stm_15$vocab == "high_school" ~ "high school",
stm_15$vocab == "ict" ~ "ICT",
stm_15$vocab == "instructional_sequence" ~ "instructional sequence",
stm_15$vocab == "intellectual_disability" ~ "intellectual disability",
stm_15$vocab == "ipad" ~ "iPad",
stm_15$vocab == "ipod" ~ "iPod",
stm_15$vocab == "kindle" ~ "Kindle",
stm_15$vocab == "learning_disability" ~ "learning disability",
stm_15$vocab == "learning_disabled" ~ "learning disabled",
stm_15$vocab == "mathematics_disability" ~ "mathematics disability",
stm_15$vocab == "mathematics_skill" ~ "mathematics skill",
stm_15$vocab == "middle_school" ~ "middle school",
stm_15$vocab == "mld" ~ "MLD",
stm_15$vocab == "more" ~ "MORE",
stm_15$vocab == "multiple_baseline" ~ "multiple baseline",
stm_15$vocab == "multiple_probe" ~ "multiple probe",
stm_15$vocab == "multiplication_fact" ~ "multiplication fact",
stm_15$vocab == "oer" ~ "OER",
stm_15$vocab == "paper_pencil" ~ "paper and pencil",
stm_15$vocab == "problem_solving" ~ "problem solving",
stm_15$vocab == "rti" ~ "RtI",
stm_15$vocab == "sbi" ~ "SBI",
stm_15$vocab == "secondary_student" ~ "secondary student",
stm_15$vocab == "self_monitoring" ~ "self-monitoring",
stm_15$vocab == "single_case" ~ "single case",
stm_15$vocab == "single_subject" ~ "single subject",
stm_15$vocab == "speech_generating" ~ "speech generating",
stm_15$vocab == "stem" ~ "STEM",
stm_15$vocab == "udl" ~ "UDL",
stm_15$vocab == "video_modeling" ~ "video modeling",
stm_15$vocab == "video_prompting" ~ "video prompting",
stm_15$vocab == "virtual_abstract" ~ "virtual-abstract",
stm_15$vocab == "virtual_manipulatives" ~ "virtual manipulatives",
stm_15$vocab == "visual_impairment" ~ "visual impairment",
stm_15$vocab == "visually_impaired" ~ "visually impaired",
stm_15$vocab == "vra" ~ "VRA",
stm_15$vocab == "word_problem" ~ "word problem",
TRUE ~ as.character(stm_15$vocab)
)
set.seed(1234)
beta_td <- tidy(stm_15, document_names = rownames(dfm_2), log = FALSE)
#### Replace integers with character values in the topic.
beta_td <- beta_td %>%
mutate(topic_label = as.factor(labeled_topic_list[beta_td$topic])) %>%
dplyr::select(topic, topic_label, term, beta)
paged_table(beta_td)2. Results
beta_td_selected <- beta_td %>%
group_by(topic_label) %>%
top_n(10, beta) %>%
arrange(topic_label, desc(beta)) %>%
mutate(beta = weights::rd(beta, add=FALSE, digits = 3))
beta_td_selected$beta <- paste0("(", beta_td_selected$beta, ")")
beta_td_selected$term <- paste(beta_td_selected$term, beta_td_selected$beta)
beta_td_selected_combined <- beta_td_selected %>%
group_by(topic_label) %>%
summarise(Terms = str_c(term, collapse =", "))
#### Examine the document-topic combinations (per-document-per topic probabilities).
set.seed(1234)
theta_td_int <- tidy(stm_15, matrix = "theta",
document_names = rownames(dfm_2@docvars$study_number))
theta_td_int$study_number <- theta_td_int$document
theta_td_int$theta <- theta_td_int$gamma
# Replace integers with character values in the topic.
theta_td <- theta_td_int %>% left_join(united_tbl, by = "study_number") %>%
mutate(topic_label = as.factor(labeled_topic_list[theta_td_int$topic]))
theta_td <- theta_td %>% dplyr::select(document, study_number, topic, topic_label,
everything())
theta_td_mean <- theta_td %>%
group_by(topic_label) %>%
summarise(theta = mean(theta)) %>%
mutate(topic = reorder(topic_label, theta))
theta_td_mean <- theta_td_mean %>% mutate(theta = round(theta, 3))
# Create correlation matrix (Correlation between topic proportion and year).
cor_topic <- cbind(stm_15$theta,
theta_td %>% dplyr::select(year)) %>% corr.test()
cor_topic <- cor_topic$r
colnames(cor_topic) <- c(labeled_topic_list, 'year')
rownames(cor_topic) <- c(labeled_topic_list, 'year')
cor_topic <- cor_topic %>% melt()
p_topic <- cbind(stm_15$theta,
theta_td %>% dplyr::select(year)) %>% corr.test()
p_topic <- p_topic$p
p_topic <- p_topic %>% melt()
p_topic$p.sig <-
case_when(
is.na(p_topic$value) ~ "",
p_topic$value < 0.001 ~ "***",
p_topic$value < 0.01 ~ "**",
p_topic$value < 0.05 ~ "*",
TRUE ~ ""
)
cor_topic <- bind_cols(cor_topic, p_topic)
names(cor_topic) <- c("labeled_topic", "labeled_topic_group", "r",
"labeled_topic_f", "labeled_topic_group_f", "p", "p.sig")
cor_topic <- cor_topic %>% dplyr::select(labeled_topic, labeled_topic_group, r, p, p.sig)
cor_topic <- cor_topic %>% filter(labeled_topic %in% 'year')
cor_topic <- cor_topic %>% filter(!labeled_topic_group %in% 'year')
cor_topic <- cor_topic %>%
mutate(p.ast = if_else(
r < 0,
str_replace_all(p.sig, '\\*', '-'),
str_replace_all(p.sig, '\\*', '+')
))
cor_topic_clean <- cor_topic %>%
mutate(r = weights::rd(r, add=FALSE, digits = 2)) %>%
mutate(p = weights::rd(p, add=FALSE, digits = 3))
cor_topic_clean$r <- paste0(cor_topic_clean$r, cor_topic_clean$p.sig)
# Merge data tables.
theta_td$document <- as.character(theta_td$document)
theta_td_mean_cor <- cor_topic_clean %>%
left_join(y = theta_td_mean, by = c("labeled_topic_group" = "topic_label")) %>%
arrange(desc(r)) %>%
ungroup()
theta_td_mean_cor <- theta_td_mean_cor %>%
select(topic, theta, r, p)
theta_td_mean_cor_beta <- theta_td_mean_cor %>%
left_join(y = beta_td_selected_combined, by = c("topic" = "topic_label")) %>%
ungroup()
theta_td_mean_cor_beta_selected <- theta_td_mean_cor_beta %>%
dplyr::select(Terms, topic, theta, r, p) %>%
mutate(theta = weights::rd(theta, add=FALSE, digits = 3)) %>%
arrange(desc(theta)) %>%
ungroup()
theta_td_mean_cor_beta_selected$theta <- paste0("(", theta_td_mean_cor_beta_selected$theta, ")")
theta_td_mean_cor_beta_selected$Topics <- paste(theta_td_mean_cor_beta_selected$topic, theta_td_mean_cor_beta_selected$theta)
theta_td_mean_cor_beta_selected_update <- theta_td_mean_cor_beta_selected %>%
select(Terms, Topics, r, p)
theta_td_mean_cor_beta_selected_update_td <- knitr::kable(theta_td_mean_cor_beta_selected_update, col.names = c("Terms", "Topics", "$r$", "$p$"))
theta_td_mean_cor_beta_selected_update_td| Terms | Topics | \(r\) | \(p\) |
|---|---|---|---|
| CAI (.042), learning disabled (.018), program (.016), learning disability (.016), strategy (.014), addition (.013), computer (.013), procedure (.010), testing (.010), subject (.009) | CAI (.111) | -.49*** | .000 |
| maintenance (.017), framework (.015), practice (.015), intellectual disability (.011), VRA (.010), EBP (.010), mathematical (.010), instructional sequence (.009), mathematics skill (.009), explicit instruction (.009) | Instructional sequence (.096) | .25*** | .000 |
| virtual manipulatives (.035), manipulatives (.023), mathematical (.015), app (.013), solving (.013), learning disability (.012), algebra (.011), area (.011), tool (.010), concrete manipulatives (.010) | Visual-based technology (.093) | .22*** | .000 |
| learning disability (.062), problem solving (.021), game (.018), MLD (.018), strategy (.008), program (.008), specific (.007), identify (.006), educational (.005), digital (.005) | Learning disabilities (.093) | .04*** | .000 |
| problem solving (.032), computer (.027), mathematical (.025), word problem (.020), intellectual disability (.019), SBI (.012), solving (.010), video (.007), practice (.007), moderate (.007) | WPS instruction (.077) | .02* | .033 |
| calculator (.050), accommodation (.021), testing (.019), assessment (.013), learning disability (.011), graphing calculator (.008), score (.008), curriculum (.007), difference (.007), access (.007) | Calculators (.070) | -.05*** | .000 |
| achievement (.032), level (.011), program (.010), measure (.009), learning disability (.007), score (.007), blended learning (.007), game (.006), engagement (.006), difference (.006) | Math achievement (.063) | -.09*** | .000 |
| testing (.015), application (.014), curriculum (.013), academic (.012), device (.011), iPad (.010), computerized (.010), behavior (.010), video prompting (.009), battery (.009) | Testing (.057) | -.03** | .006 |
| measure (.021), robot (.021), device (.018), communication (.011), assistive technology (.010), speech generating (.010), level (.008), system (.007), task (.007), assessment (.006) | Robotics (.054) | .04** | .003 |
| system (.017), blind (.011), shape (.010), relation (.008), sonification (.008), understanding (.008), tool (.007), environment (.007), geometry (.007), FXS (.006) | Auditory support (.051) | .02 | .072 |
| software (.034), geometry (.010), development (.010), number (.010), training (.010), computer (.008), game (.008), instructional (.007), level (.007), assessment (.007) | Software (.050) | -.14*** | .000 |
| information (.022), material (.012), image (.012), visually impaired (.012), knowledge (.011), STEM (.010), access (.008), graph (.008), graphics (.008), field (.008) | Support for visual materials (.048) | .07*** | .000 |
| app (.011), environment (.010), educational (.010), online (.010), pupil (.010), comparison (.010), need (.009), special (.009), time (.008), practice (.008) | Apps (.047) | .05*** | .000 |
| self-monitoring (.020), tool (.019), access (.017), assignment (.014), visual impairment (.014), digital text (.013), algebra (.011), homework (.011), phase (.011), textbook (.010) | Self-monitoring tools (.045) | .02* | .033 |
| fractions (.086), accuracy (.026), solving (.025), middle school (.023), EAI (.021), functional relation (.019), addition (.018), instructional sequence (.017), virtual-abstract (.017), session (.016) | Fractions instruction (.044) | .14*** | .000 |
3. Distribution of topic probability
theta_td$period <- dplyr::case_when(
theta_td$period == "1980_to_1989" ~ "1980 to 1989",
theta_td$period == "1990_to_1999" ~ "1990 to 1999",
theta_td$period == "2000_to_2009" ~ "2000 to 2009",
theta_td$period == "2010_to_2021" ~ "2010 to 2021",
TRUE ~ as.character(theta_td$period)
)
topic_by_prevalence_publication <- theta_td %>%
ggplot(aes(study_number, theta, color = as.factor(period))) +
geom_line(show.legend = TRUE, size = 0.3) +
lemon::facet_rep_wrap(~ topic_label, repeat.tick.labels = FALSE, ncol = 3) +
labs(x = "Publications",
y = expression("Topic proportion" ~ (theta)),
color = "Period",
size = 11) +
scale_y_continuous(labels = numform::ff_num(zero = 0, digits = 2)) +
theme_minimal(base_size = 11) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(color = "#3B3B3B", size = 0.3),
axis.ticks = element_line(color = "#3B3B3B", size = 0.3),
strip.text.x = element_text(size = 11, color = "#3B3B3B"),
axis.text.x = element_text(size = 11, color = "#3B3B3B"),
axis.text.y = element_text(size = 11, color = "#3B3B3B"),
axis.title = element_text(size = 11, color = "#3B3B3B"),
axis.title.x = element_text(margin = margin(t = 10)),
axis.title.y = element_text(margin = margin(r = 10)),
legend.title = element_text(size = 11, color = "#3B3B3B"),
legend.text = element_text(size = 11, color = "#3B3B3B"),
legend.title.align = 0.5,
legend.position = "bottom")
topic_by_prevalence_publication 
4. Topic proportion by year
effects_year <- get_effects(estimates = effect_stm_15,
variable = 'year',
type = 'pointestimate')
effects_year <- effects_year %>%
mutate(topic_label = labeled_topic_list[as.character(effects_year$topic)]) %>%
dplyr::select(value, topic, topic_label, proportion, lower, upper)
effects_year <- effects_year %>%
mutate(year = as.numeric(effects_year$value) + 1979)
year_plot <- effects_year %>%
ggplot(aes(x = year, y = proportion)) +
facet_wrap(~ topic_label, ncol = 3, scales = "free") +
scale_y_continuous(labels = numform::ff_num(zero = 0, digits = 3)) +
geom_line(color = "#3B3B3B", size = 0.3) +
xlab('Publication year') +
ylab(expression("Topic proportion" ~ (theta))) +
theme_minimal(base_size = 11) +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(color = "#3B3B3B", size = 0.3),
axis.ticks = element_line(color = "#3B3B3B", size = 0.3),
strip.text.x = element_text(size = 11, color = "#3B3B3B"),
axis.text.x = element_text(size = 11, color = "#3B3B3B"),
axis.text.y = element_text(size = 11, color = "#3B3B3B"),
axis.title = element_text(size = 11, color = "#3B3B3B"),
axis.title.x = element_text(margin = margin(t = 10)),
axis.title.y = element_text(margin = margin(r = 10)))
year_plot 
Word network analysis
Replace character vectors
dfm_2@Dimnames$features <- dplyr::case_when(
dfm_2@Dimnames$features == "aac" ~ "AAC",
dfm_2@Dimnames$features == "adhd" ~ "ADHD",
dfm_2@Dimnames$features == "asd" ~ "ASD",
dfm_2@Dimnames$features == "assistive_technology" ~ "assistive technology",
dfm_2@Dimnames$features == "baip" ~ "BAIP",
dfm_2@Dimnames$features == "basic_skill" ~ "basic skill",
dfm_2@Dimnames$features == "blended_learning" ~ "blended learning",
dfm_2@Dimnames$features == "bmt" ~ "BMT",
dfm_2@Dimnames$features == "cai" ~ "CAI",
dfm_2@Dimnames$features == "cbm" ~ "CBM",
dfm_2@Dimnames$features == "ccss" ~ "CCSS",
dfm_2@Dimnames$features == "concrete_manipulatives" ~ "concrete manipulatives",
dfm_2@Dimnames$features == "cra" ~ "CRA",
dfm_2@Dimnames$features == "digital_text" ~ "digital text",
dfm_2@Dimnames$features == "drill_practice" ~ "drill and practice",
dfm_2@Dimnames$features == "eai" ~ "EAI",
dfm_2@Dimnames$features == "ebd" ~ "EBD",
dfm_2@Dimnames$features == "ebp" ~ "EBP",
dfm_2@Dimnames$features == "eworkbook" ~ "eWorkbook",
dfm_2@Dimnames$features == "explicit_instruction" ~ "explicit instruction",
dfm_2@Dimnames$features == "functional_relation" ~ "functional relation",
dfm_2@Dimnames$features == "fxs" ~ "FXS",
dfm_2@Dimnames$features == "graphing_calculator" ~ "graphing calculator",
dfm_2@Dimnames$features == "high_school" ~ "high school",
dfm_2@Dimnames$features == "ict" ~ "ICT",
dfm_2@Dimnames$features == "instructional_sequence" ~ "instructional sequence",
dfm_2@Dimnames$features == "intellectual_disability" ~ "intellectual disability",
dfm_2@Dimnames$features == "ipad" ~ "iPad",
dfm_2@Dimnames$features == "ipod" ~ "iPod",
dfm_2@Dimnames$features == "kindle" ~ "Kindle",
dfm_2@Dimnames$features == "learning_disability" ~ "learning disability",
dfm_2@Dimnames$features == "learning_disabled" ~ "learning disabled",
dfm_2@Dimnames$features == "mathematics_disability" ~ "mathematics disability",
dfm_2@Dimnames$features == "mathematics_skill" ~ "mathematics skill",
dfm_2@Dimnames$features == "middle_school" ~ "middle school",
dfm_2@Dimnames$features == "mld" ~ "MLD",
dfm_2@Dimnames$features == "more" ~ "MORE",
dfm_2@Dimnames$features == "multiple_baseline" ~ "multiple baseline",
dfm_2@Dimnames$features == "multiple_probe" ~ "multiple probe",
dfm_2@Dimnames$features == "multiplication_fact" ~ "multiplication fact",
dfm_2@Dimnames$features == "oer" ~ "OER",
dfm_2@Dimnames$features == "paper_pencil" ~ "paper and pencil",
dfm_2@Dimnames$features == "problem_solving" ~ "problem solving",
dfm_2@Dimnames$features == "rti" ~ "RtI",
dfm_2@Dimnames$features == "sbi" ~ "SBI",
dfm_2@Dimnames$features == "secondary_student" ~ "secondary student",
dfm_2@Dimnames$features == "self_monitoring" ~ "self-monitoring",
dfm_2@Dimnames$features == "single_case" ~ "single case",
dfm_2@Dimnames$features == "single_subject" ~ "single subject",
dfm_2@Dimnames$features == "speech_generating" ~ "speech generating",
dfm_2@Dimnames$features == "stem" ~ "STEM",
dfm_2@Dimnames$features == "udl" ~ "UDL",
dfm_2@Dimnames$features == "video_modeling" ~ "video modeling",
dfm_2@Dimnames$features == "video_prompting" ~ "video prompting",
dfm_2@Dimnames$features == "virtual_abstract" ~ "virtual-abstract",
dfm_2@Dimnames$features == "virtual_manipulatives" ~ "virtual manipulatives",
dfm_2@Dimnames$features == "visual_impairment" ~ "visual impairment",
dfm_2@Dimnames$features == "visually_impaired" ~ "visually impaired",
dfm_2@Dimnames$features == "vra" ~ "VRA",
dfm_2@Dimnames$features == "word_problem" ~ "word problem",
TRUE ~ as.character(dfm_2@Dimnames$features)
)
dfm_2_td <- tidy(dfm_2)
dfm_gamma <- dfm_2_td %>%
left_join(y = theta_td, by = c("document" = "document"))1. 1980 to 1989.
word_co_by_1989 <- dfm_gamma %>%
filter(year >=1980 & year <= 1989) %>%
pairwise_count(term, study_number, sort = TRUE)
# summary(word_co_by_1989)
set.seed(1234)
graph_1989_td <- word_co_by_1989 %>%
filter(n >= 4) %>%
as_tbl_graph(directed = TRUE) %>%
mutate(centrality = centrality_degree(mode = "out") / (39-1))
set.seed(1234)
layout_1989 <- create_layout(graph_1989_td,
layout = "stress")
set.seed(1234)
network_1989 <- layout_1989 %>%
ggraph() +
geom_edge_link(aes(edge_alpha = n), edge_colour = "#bdbdbd", edge_width = 1) +
geom_node_point(aes(size = centrality, colour = centrality)) +
geom_node_text(aes(label = name), size = 4, repel = TRUE) +
scale_color_continuous(guide = 'legend', high = "#3182bd", low = "#deebf7") +
theme_void(base_size = 13) +
theme(legend.title=element_blank(),
legend.text=element_text(size = 11),
legend.position = "right")
network_1989 
2. 1990 to 1999.
word_co_by_1999 <- dfm_gamma %>%
filter(year >=1990 & year <= 1999) %>%
pairwise_count(term, study_number, sort = TRUE)
# summary(word_co_by_1999)
set.seed(1234)
graph_1999_td <- word_co_by_1999 %>%
filter(n >= 4) %>%
as_tbl_graph(directed = TRUE) %>%
mutate(centrality = centrality_degree(mode = "out") / (43-1))
set.seed(1234)
layout_1999 <- create_layout(graph_1999_td,
layout = "stress")
set.seed(1234)
network_1999 <- layout_1999 %>%
ggraph() +
geom_edge_link(aes(edge_alpha = n), edge_colour = "#bdbdbd", edge_width = 1) +
geom_node_point(aes(size = centrality, colour = centrality)) +
geom_node_text(aes(label = name), size = 4, repel = TRUE) +
scale_color_continuous(guide = 'legend', high = "#3182bd", low = "#deebf7") +
theme_void(base_size = 13) +
theme(legend.title=element_blank(),
legend.text=element_text(size = 11),
legend.position = "right")
network_1999 
3. 2000 to 2009.
word_co_by_2009 <- dfm_gamma %>%
filter(year >=2000 & year <= 2009) %>%
pairwise_count(term, study_number, sort = TRUE)
# summary(word_co_by_2009)
set.seed(1234)
graph_2009_td <- word_co_by_2009 %>%
filter(n >= 4) %>%
as_tbl_graph(directed = TRUE) %>%
mutate(centrality = centrality_degree(mode = "out") / (52-1)) # check
set.seed(1234)
layout_2009 <- create_layout(graph_2009_td,
layout = "stress")
set.seed(1234)
network_2009 <- layout_2009 %>%
ggraph() +
geom_edge_link(aes(edge_alpha = n), edge_colour = "#bdbdbd", edge_width = 1) +
geom_node_point(aes(size = centrality, colour = centrality)) +
geom_node_text(aes(label = name), size = 4, repel = TRUE) +
scale_color_continuous(guide = 'legend', high = "#3182bd", low = "#deebf7") +
theme_void(base_size = 13) +
theme(legend.title=element_blank(),
legend.text=element_text(size = 11),
legend.position = "right")
network_2009 
4. 2010 to 2021.
word_co_by_2021 <- dfm_gamma %>%
filter(year >=2010 & year <= 2021) %>%
pairwise_count(term, study_number, sort = TRUE)
# summary(word_co_by_2021)
set.seed(1234)
graph_2021_td <- word_co_by_2021 %>%
filter(n >= 14) %>%
as_tbl_graph(directed = TRUE) %>%
mutate(centrality = centrality_degree(mode = "out") / (51-1)) # check
set.seed(1234)
layout_2021 <- create_layout(graph_2021_td,
layout = "stress")
set.seed(1234)
network_2021 <- layout_2021 %>%
ggraph() +
geom_edge_link(aes(edge_alpha = n), edge_colour = "#bdbdbd", edge_width = 1) +
geom_node_point(aes(size = centrality, colour = centrality)) +
geom_node_text(aes(label = name), size = 4, repel = TRUE) +
scale_color_continuous(guide = 'legend', high = "#3182bd", low = "#deebf7") +
theme_void(base_size = 13) +
theme(legend.title=element_blank(),
legend.text=element_text(size = 11),
legend.position = "right")
network_2021
Total number of publications
Number of publications over time
year_doc_counts_all <- united_tbl %>%
group_by(year) %>%
summarize(publication_number = n()) %>%
ungroup()
plot_year_doc_counts <- year_doc_counts_all %>%
ggplot(aes(year, publication_number)) +
geom_col(fill = "#979797") +
theme_classic() +
theme(
axis.line = element_line(color = "#3B3B3B", size = 0.2),
axis.title = element_text(size = 11, color = "#3B3B3B"),
axis.title.x = element_text(margin = margin(t = 10), color = "#3B3B3B"),
axis.title.y = element_text(margin = margin(r = 10), color = "#3B3B3B")) +
labs(x = "Year", y = "Number of publications")
plot_year_doc_counts %>% ggplotly()